Processing apparatus, management apparatus, lithography apparatus, and article manufacturing method

ABSTRACT

A processing apparatus includes a driver configured to drive a controlled object, and a controller configured to control the driver by generating a command value to the driver based on a control error. The controller includes a first compensator configured to generate a first command value based on the control error, a second compensator configured to generate a second command value based on the control error, and an adder configured to obtain the command value by adding the first command value and the second command value. The second compensator includes a neural network for which a parameter value is decided by learning, and input parameters input to the neural network include at least one of a driving condition of the driver and an environment condition in a periphery of the controlled object in addition to the control error.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates to a processing apparatus, a managementapparatus, a lithography apparatus, and an article manufacturing method.

Description of the Related Art

There are provided various techniques for improving the accuracy ofcontrol of a controlled object. Japanese Patent Laid-Open No.2006-128685 describes that in a control system obtained by combining afeedback controller and a feedforward controller, the parameters of thefeedforward controller are updated by iterative learning.

In recent years, there is proposed a technique of controlling acontrolled object using a neural network and improving the accuracy. Theneural network is optimized for each controlled object. The optimizedneural network is called a learned network. The controlled object iscontrolled using the learned neural network.

A control apparatus using a neural network can decide the parametervalues of the neural network by performing reinforcement learning.However, since the state of a controlled object can change over time,even the neural network optimized at a given time is no longer optimumsince the state of the controlled object has changed thereafter.Therefore, the control accuracy of the control apparatus may deterioratedue to the change in the state of the controlled object.

SUMMARY OF THE INVENTION

The present invention provides a technique advantageous in suppressingdeterioration in control accuracy caused by a change in the state of acontrolled object.

The present invention in its one aspect provides a processing apparatuscomprising a driver configured to drive a controlled object, and acontroller configured to control the driver by generating a commandvalue to the driver based on a control error, wherein the controllerincludes a first compensator configured to generate a first commandvalue based on the control error, a second compensator configured togenerate a second command value based on the control error, and an adderconfigured to obtain the command value by adding the first command valueand the second command value, the second compensator includes a neuralnetwork for which a parameter value is decided by learning, and inputparameters input to the neural network include at least one of a drivingcondition of the driver and an environment condition in a periphery ofthe controlled object in addition to the control error.

Further features of the present invention will become apparent from thefollowing description of exemplary embodiments (with reference to theattached drawings).

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing the configuration of a manufacturingsystem,

FIG. 2 is a block diagram showing the arrangement of a processingapparatus:

FIG. 3 is a block diagram exemplifying the arrangement of the processingapparatus shown in FIG. 2 ;

FIG. 4 is a flowchart exemplifying the operation of a managementapparatus in a learning sequence;

FIG. 5 is a view exemplifying the arrangement of a scanning exposureapparatus;

FIG. 6 is a flowchart exemplifying the operation of the scanningexposure apparatus in an actual sequence; and

FIGS. 7A and 7B are graphs showing the reduction effect of a controlerror of a controlled object according to an embodiment.

DESCRIPTION OF THE EMBODIMENTS

Hereinafter, embodiments will be described in detail with reference tothe attached drawings. Note, the following embodiments are not intendedto limit the scope of the claimed invention. Multiple features aredescribed in the embodiments, but limitation is not made to an inventionthat requires all such features, and multiple such features may becombined as appropriate. Furthermore, in the attached drawings, the samereference numerals are given to the same or similar configurations, andredundant description thereof is omitted.

First Embodiment

FIG. 1 shows the configuration of a manufacturing system MS according tothe embodiment. The manufacturing system MS can include, for example, aprocessing apparatus 1, a control apparatus 2 that controls theprocessing apparatus 1, and a management apparatus (learning apparatus)3 that manages the processing apparatus 1 and the control apparatus 2.The processing apparatus 1 is an apparatus that executes processing fora processing target object like a manufacturing apparatus, an inspectionapparatus, a monitoring apparatus, or the like. The concept of theprocessing can include processing, inspection, monitoring, andobservation of a processing target object.

The processing apparatus 1 can include a controlled object and controlthe controlled object using a neural network for which parameter valuesare decided by reinforcement learning. The control apparatus 2 can beconfigured to send a driving command to the processing apparatus 1 andreceive a driving result or a control result from the processingapparatus 1. The management apparatus 3 can perform reinforcementlearning of deciding a plurality of parameter values of the neuralnetwork of the processing apparatus 1. More specifically, the managementapparatus 3 can decide the plurality of parameter values of the neuralnetwork by repeating an operation of sending a driving command to theprocessing apparatus 1 and receiving a driving result from theprocessing apparatus 1 while changing all or some of the plurality ofparameter values. The management apparatus 3 may be understood as alearning apparatus.

All or some of the functions of the control apparatus 2 may beincorporated in the management apparatus 3. All or some of the functionsof the control apparatus 2 may be incorporated in the processingapparatus 1. The processing apparatus 1, the control apparatus 2, andthe management apparatus 3 may be formed physically integrally orseparately. The processing apparatus 1 may be controlled by the controlapparatus 2 as a whole, or may include components controlled by thecontrol apparatus 2 and those not controlled by the control apparatus 2.

FIG. 2 exemplifies the arrangement of the processing apparatus 1. Theprocessing apparatus 1 can include a stage mechanism 5 including a stage(holder) ST as a controlled object. The processing apparatus 1 canfurther include a sensor 6 that detects the position or state of thestage ST, a driver 7 that drives the stage mechanism 5, and a controller8 that receives an output from the sensor 6 and gives a command value tothe driver 7. The stage ST can hold a positioning target object. Thestage ST can be guided by a guide (not shown). The stage mechanism 5 caninclude an actuator AC that moves the stage ST. The driver 7 drives theactuator AC. More specifically, for example, the driver 7 can supply, tothe actuator AC, a current (electric energy) corresponding to thecommand value given from the controller 8. The actuator AC can move thestage ST by a force (mechanical energy) corresponding to the currentsupplied from the driver 7. The controller 8 can control the position orstate of the stage ST as the controlled object using the neural networkfor which the parameter values are decided by reinforcement learning. Adriving commander 9 gives the driving target position of the stage ST asthe controlled object and the driving condition of the driver 7 to thecontroller 8. An environment sensor 10 detects the environment conditionin the periphery of the stage ST and gives the detected environmentcondition to the controller 8.

FIG. 3 is a block diagram exemplifying the arrangement of the processingapparatus 1 shown in FIG. 2 . The controller 8 can include a subtracter81, a first compensator 82, a second compensator (neural network) 83,and an adder 84. The subtracter 81 can calculate a control error as adifference between the driving command (for example, the targetposition) given from the control apparatus 2 and the detection result(for example, the position of the stage ST) output from the sensor 6.The first compensator 82 can generate the first command value byperforming compensation calculation for the control error provided fromthe subtracter 81. The second compensator 83 includes a neural network.The neural network can input, as input parameters, the driving conditioninput from the driving commander 9 and the environment conditionmeasured by the environment sensor 10 in addition to the control errorprovided from the subtracter 81, and generate the second command valueby performing compensation calculation. The driving condition and theenvironment condition are pieces of information that are not input tothe first compensator 82. The adder 84 can generate the command value byadding the first command value and the second command value. Thecontroller 8, the driver 7, the stage mechanism 5, and the sensor 6 forma feedback control system that controls the stage ST as the controlledobject based on the control error.

The first compensator 82 can be, for example, a PID compensator but maybe another compensator. When, for example, L represents the number ofinputs, M represents the number of intermediate layers, and N representsthe number of outputs (L, M. and N are all positive integers), thesecond compensator 83 can be, for example, a neural network defined bythe product of an L×M matrix and an M×N matrix. The plurality ofparameter values of the neural network can be decided or updated byreinforcement learning executed by the management apparatus 3. The firstcompensator 82 is not always necessary, and only the second compensator83 may generate the command value to be given to the driver 7.

The driving condition input from the driving commander 9 can include,for example, at least one of the current position, target position,driving direction, driving stroke, speed, acceleration, jerk, and snapof the stage but may be another driving condition. Furthermore, thecondition may be a value such as a maximum, average, or variance in aseries of driving operations. Alternatively, the condition may be thecurrent value, a past history at a specific time, or a future targetvalue after a given time elapses.

The environment condition input from the environment sensor 10 caninclude, for example, at least one of the pressure, temperature,humidity, vibration, wind speed, and flow rate in the periphery of thestage ST but may be another condition as long as it can be measured by asensor. The value may be the current value, a past value a given timebefore, or a future value predicted from the past change. A valueobtained by performing filtering processing for the measured value ofthe sensor may be used.

The management apparatus 3 can function as a learning device or arelearning device that executes a learning sequence when a rewardrequired from the control result of the stage ST by the controller 8 ofthe processing apparatus 1 does not satisfy a predetermined criterion.In the learning sequence, a parameter value set constituted by theplurality of parameter values of the second compensator (neural network)83 can be decided or redecided by reinforcement learning.

FIG. 4 exemplifies the operation of the management apparatus 3 in thelearning sequence. In step S101, the management apparatus 3 caninitialize the plurality parameter values (parameter value set) of thesecond compensator (neural network) 83. In step S102, the managementapparatus 3 can send a command to the processing apparatus 1 to drivethe stage ST as the controlled object. More specifically, in step S102,the management apparatus 3 can send a driving command to the controller8 of the processing apparatus 1 via the control apparatus 2. In responseto this, the controller 8 of the processing apparatus 1 can cause thedriver 7 to drive the stage ST in accordance with the driving command,thereby controlling the position of the stage ST.

In step S103, the management apparatus 3 can acquire, from thecontroller 8 of the processing apparatus 1 via the control apparatus 2,driving data indicating the driving state of the stage ST as thecontrolled object in step S102. The driving data can include, forexample, at least one of the output from the sensor 6 and the outputfrom the subtracter 81. In step S104, the management apparatus 3 cancalculate a reward based on the driving data acquired in step S103. Thereward can be calculated based on a predefined formula. For example, ifthe reward is calculated based on the control error, the reward can becalculated in accordance with a formula that gives the reciprocal of thecontrol error, a formula that gives the reciprocal of the logarithm ofthe control error, a formula that gives the reciprocal of the quadraticfunction of the control error, or the like, but may be calculated inaccordance with another formula. In one example, as the value of thereward is larger, the second compensator (neural network) 83 is moresuperior. Conversely, as the value of the reward is smaller, the secondcompensator (neural network) 83 may be more superior.

In step S105, the management apparatus 3 generates a new parameter valueset by changing at least one of the plurality of parameter values of thesecond compensator (neural network) 83, and sets the new parametervalues in the second compensator (neural network) 83. Steps S106, S107,and S108 can be the same as steps S102, S103, and S104, respectively. Instep S106, the management apparatus 3 can send a command to theprocessing apparatus 1 to drive the stage ST. More specifically, in stepS106, the management apparatus 3 can send a driving command to thecontroller 8 of the processing apparatus 1 via the control apparatus 2.In response to this, the controller 8 of the processing apparatus 1 cancause the driver 7 to drive the stage ST in accordance with the drivingcommand, thereby controlling the position of the stage ST. In step S107,the management apparatus 3 can acquire, from the controller 8 of theprocessing apparatus 1 via the control apparatus 2, driving dataindicating the driving state of the stage ST in step S106. In step S108,the management apparatus 3 can calculate a reward based on the drivingdata acquired in step S107.

In step S109, the management apparatus 3 determines whether the rewardcalculated in step S108 is improved, as compared with the rewardcalculated in step S104. Then, if the reward calculated in step S108 isimproved, as compared with the reward calculated in step S104, themanagement apparatus 3 adopts, in step S110, as the latest parametervalues, the parameter value set obtained after the change operation isexecuted in step S105. On the other hand, if the reward calculated instep S108 is not improved, as compared with the reward calculated instep S104, the management apparatus 3 does not adopt, in step S111, theparameter value set obtained after the change operation is executed instep S105, and returns to step S105. In this case, in step S105, a newparameter value set is set in the second compensator (neural network)83.

If step S110 is executed, the management apparatus 3 determines in stepS112 whether the reward calculated in step S108 immediately precedinglyexecuted satisfies the predetermined criterion. If the reward satisfiesthe predetermined criterion, the processing shown in FIG. 4 ends. Thismeans that the parameter value set generated in step S105 immediatelyprecedingly executed is decided as the parameter value set afterreinforcement learning. The neural network set with the parameter valueset after reinforcement learning can be called a learned model. On theother hand, if it is determined in step S112 that the reward calculatedin step S108 immediately precedingly executed does not satisfy thepredetermined criterion, the management apparatus 3 repeats theprocesses from step S105.

The driving condition and the environment condition when executing stepS102 are not constant, and some or all of the possible conditions can bechanged. That is, the management apparatus 3 (learning device) repeatsreinforcement learning while changing at least one of the drivingcondition and the environment condition. Furthermore, in the learningstep, learning is repeatedly executed while changing a combinationpattern among a first number of first combination patterns of thedriving condition and the environment condition. If the reward exceeds apredetermined value while learning is repeatedly executed, thecombination patterns of the driving condition and the environmentcondition may be increased. That is, in this case, learning may berepeatedly executed while changing a combination pattern among a secondnumber of second combination patterns, which is larger than the firstnumber.

The present inventor found that even if the history of the control erroris the same, the future behavior of the controlled object may change dueto a difference in the driving condition of the controlled object or theenvironment condition in the periphery. In this embodiment, to cope withsuch case, the input parameters input to the neural network can includeat least one of the driving condition and the environment condition inaddition to the control error. Thus, the neural network is learned tooutput the command value to the driver 7 that can suppress the controlerror.

The processing apparatus 1 can operate, in a sequence (to be referred toas an actual sequence hereinafter) of executing processing for theprocessing target object, as an apparatus including the learned model(second compensator 83) obtained in the above-described learningsequence. In one example, the processing apparatus 1 can execute theactual sequence under management of the management apparatus 3. However,in another example, the processing apparatus 1 can execute the actualsequence independently of management of the management apparatus 3.

Second Embodiment

An example in which the above-described manufacturing system MS isapplied to a scanning exposure apparatus 500 shown in FIG. 5 will bedescribed below with reference to FIG. 6 . The scanning exposureapparatus 500 is a step-and-scan exposure apparatus that performsscanning exposure of a substrate 14 by slit light shaped by a slitmember. The scanning exposure apparatus 50) can include an illuminationoptical system 23, an original stage mechanism 12, a projection opticalsystem 13, a substrate stage mechanism 15, a first position measurementdevice 17, a second position measurement device 18, a substrate markmeasurement device 21, a substrate conveyer 22, and a controller 25.

The controller 25 controls the illumination optical system 23, theoriginal stage mechanism 12, the projection optical system 13, thesubstrate stage mechanism 15, the first position measurement device 17,the second position measurement device 18, the substrate markmeasurement device 21, and the substrate conveyer 22. The controller 25controls processing of transferring a pattern of an original 11 to thesubstrate 14. Furthermore, the controller 25 can include the function ofthe controller 8 according to the first embodiment. The controller 25can be formed by, for example, a Programmable Logic Device (PLD) such asa Field Programmable Gate Array (FPGA), an Application SpecificIntegrated Circuit (ASIC), a general-purpose computer installed with aprogram, or a combination of all or some of these components.

The original stage mechanism 12 can include an original stage RST thatholds the original 11, and a first actuator RAC that drives the originalstage RST. The substrate stage mechanism 15 can include a substratestage WST that holds the substrate 14, and a second actuator WAC thatdrives the substrate stage WST. The illumination optical system 23illuminates the original 11. The illumination optical system 23 shapes,by a light shielding member such as a masking blade, light emitted froma light source (not shown) into, for example, band-like or arcuate slitlight long in the X direction, and illuminates a portion of the original11 with this slit light. The original 11 and the substrate 14 are heldby the original stage RST and the substrate stage WST, respectively, andarranged at almost optically conjugate positions (on the object planeand image plane of the projection optical system 13) via the projectionoptical system 13.

The projection optical system 13 has a predetermined projectionmagnification (for example, 1, ½, or ¼), and projects the pattern of theoriginal 11 on the substrate 14 by the slit light. A region (a regionirradiated with the slit light) on the substrate 14 where the pattern ofthe original 11 is projected can be called an irradiation region. Theoriginal stage RST and the substrate stage WST are configured to bemovable in a direction (Y direction) orthogonal to the optical axisdirection (Z direction) of the projection optical system 13. Theoriginal stage RST and the substrate stage WST are relatively scanned ata velocity ratio corresponding to the projection magnification of theprojection optical system 13 in synchronism with each other. This scansthe substrate 14 in the Y direction with respect to the irradiationregion, thereby transferring the pattern formed on the original 11 to ashot region of the substrate 14. Then, by sequentially performing suchscanning exposure for the plurality of shot regions of the substrate 14while moving the substrate stage WST, the exposure processing for theone substrate 14 is completed.

The first position measurement device 17 includes, for example, a laserinterferometer, and measures the position of the original stage RST. Forexample, the laser interferometer irradiates, with a laser beam, areflecting plate (not shown) provided in the original stage RST, anddetects a displacement (a displacement from a reference position) of theoriginal stage RST by interference between the laser beam reflected bythe reflecting plate and the laser beam reflected by a referencesurface. The first position measurement device 17 can acquire thecurrent position of the original stage RST based on the displacement. Inthis example, the first position measurement device 17 may measure theposition of the original stage RST by a position measurement device, forexample, an encoder instead of the laser interferometer. The substratemark measurement device 21 includes, for example, an optical system andan image sensor, and can detect the position of a mark provided on thesubstrate 14.

The second position measurement device 18 includes, for example, a laserinterferometer, and measures the position of the substrate stage WST.For example, the laser interferometer irradiates, with a laser beam, areflecting plate (not shown) provided in the substrate stage WST, anddetects a displacement (a displacement from a reference position) of thesubstrate stage WST by interference between the laser beam reflected bythe reflecting plate and the laser beam reflected by a referencesurface. The second position measurement device 18 can acquire thecurrent position of the substrate stage WST based on the displacement.In this example, the second position measurement device 18 may measurethe position of the substrate stage WST by a position measurementdevice, for example, an encoder instead of the laser interferometer.

Sensors 30, 31, and 32 are arranged near a controlled object, and candetect a pressure, temperature, humidity, vibration, wind speed, flowrate, and the like as the environment condition in the periphery of thecontrolled object. In the example shown in FIG. 5 , the sensor 30 isarranged near the substrate stage WST, the sensor 31 is arranged nearthe original stage RST, and the sensor 32 is arranged near theprojection optical system 13.

The scanning exposure apparatus 500 is required to accurately transferthe pattern of the original 11 to the target position of the substrate14. To achieve this, it is important to accurately control the relativeposition of the original 11 on the original stage RST with respect tothe substrate 14 on the substrate stage WST during scanning exposure.Therefore, as a reward, a value for evaluating the relative positionerror (synchronous error) between the original stage RST and thesubstrate stage WST can be adopted. To improve the detection accuracy ofthe mark of the substrate 14, it is important to accurately position thesubstrate stage WST under the substrate mark measurement device 21.Therefore, as a reward, a value for evaluating the control error of thesubstrate stage WST while the mark is imaged can be adopted. To improvethe throughput, it is important to increase the conveyance speed of thesubstrate. Furthermore, at the time of loading and unloading thesubstrate, it is important that the control errors of the substrateconveyer 22 and the substrate stage WST converge to a predeterminedvalue or less in a short time after the completion of driving.Therefore, as a reward, a value for evaluating the convergence times ofthe substrate conveyer 22 and the substrate stage WST can be adopted.Each of the substrate stage mechanism 15, the original stage mechanism12, and the substrate conveyer 22 is an example of an operation unitthat performs an operation for the processing of transferring thepattern of the original 11 to the substrate 14.

FIG. 6 exemplifies the actual sequence of the scanning exposureapparatus 500. This actual sequence starts when the management apparatus3 instructs the controller 25 of the scanning exposure apparatus 500 toexecute the actual sequence (substrate processing sequence). Thesubstrate processing sequence can include, for example, steps S301.S302, and S303 as a plurality of sub-sequences.

In step S301, the controller 25 controls the substrate conveyer 22 toload (convey) the substrate 14 to the substrate stage WST. In step S302,the controller 25 can control the substrate stage mechanism 15 so thatthe mark of the substrate 14 falls within the field of view of thesubstrate mark measurement device 21, and control the substrate markmeasurement device 21 to detect the position of the mark of thesubstrate 14. This operation can be executed for each of the pluralityof marks of the substrate 14. In step S303, the controller 25 controlsthe substrate stage mechanism 15, the original stage mechanism 12, theillumination optical system 23, and the like so that the pattern of theoriginal 11 is transferred to each of the plurality of shot regions ofthe substrate 14 (exposure step). In step S304, the controller 25controls the substrate conveyer 22 to unload (convey) the substrate 14on the substrate stage WST.

In step S301, in order for the substrate conveyer 22 to accurately placethe substrate 14 on the substrate stage WST, the positioning accuracy ofthe substrate conveyer 22 is required. In this case, as the drivingcondition input to the second compensator 83, the speed, acceleration,and jerk of the substrate conveyer 22 can be obtained. As theenvironment condition, a pressure when the substrate conveyer 22 sucksthe substrate 14, or an output from an acceleration sensor provided onthe substrate conveyer 22 when the substrate conveyer 22 is driven canbe obtained.

In step S302, it is required that the error of the substrate stage WSTconverges as quickly as possible by driving the substrate stage WST sothat the mark on the substrate 14 is located immediately under thesubstrate mark measurement device 21. In this case, the drivingcondition input to the second compensator 83 can be, for example, atleast one of the speed, acceleration, and jerk of the substrate stageWST. Alternatively, the driving condition may be at least one of thedirection and the distance when, in the state in which a given mark islocated immediately under the substrate mark measurement device 21, thesubstrate stage WST is driven so that the mark to be measured next islocated immediately under the substrate mark measurement device 21.Furthermore, the environment condition can be at least one of a changein pressure in a space measured by a pressure sensor when the substratestage WST is driven, and an output from an acceleration sensor providednear the substrate mark measurement device.

In step S303, the driving condition input to the second compensator 83can be at least one of the following pieces of information.

-   -   Coordinates for specifying a shot region as an exposure target        on the substrate    -   The size of a shot region in the X direction and/or the Y        direction    -   The speed, acceleration, jerk, and/or driving direction when        moving the substrate stage or the original stage at the time of        exposure

The environment condition input to the second compensator 83 can be atleast one of the strength of exposure light with which the substrate isirradiated, and the pressure, temperature, humidity, vibration, windspeed, and flow rate detected by the sensors 30, 31, and 32.

Examples of the controlled object for which a neural network is formedare the substrate stage mechanism 15, the original stage mechanism 12,and the substrate conveyer 22 but a neural network may be incorporatedin another component. For example, a plurality of components such as thesubstrate stage mechanism 15, the original stage mechanism 12, and thesubstrate conveyer 22 may be controlled by one neural network or theplurality of components may be controlled by different neural networks,respectively. Furthermore, as a learned model, the same learned model ordifferent learned models may be used for the conveyance sequence, themeasurement sequence, and the exposure sequence. In calculation of areward, the same formula or different formulas may be used for theconveyance sequence, the measurement sequence, and the exposuresequence.

FIGS. 7A and 7B are graphs showing the reduction effect of the controlerror of the controlled object when this embodiment is applied. FIG. 7Ashows time profiles of the acceleration as one driving condition. Whencomparing a graph 600 indicated by a solid line and a graph 601indicated by a broken line with each other, a change in acceleration isthe same up to a midpoint but the timing of returning the accelerationto 0 is different. Referring to FIG. 7B, a graph 603 represents thecontrol error when the controlled object is driven with the accelerationprofile of the graph 600, and a graph 604 represents the control errorwhen the controlled object is driven with the acceleration profile ofthe graph 601. In a section 602 where the acceleration condition is thesame, the graphs 603 and 604 indicate the identical error waveforms buterror transitions become different from each other in the middle. Evenif the error history is the same but a difference is generated in theerror due to a difference in the future acceleration condition, it ispossible to provide a neural network capable of suppressing the error byinputting the acceleration condition to the neural network.

The example in which the manufacturing system MS is applied to thescanning exposure apparatus 500 has been explained above. However, themanufacturing system MS may be applied to an exposure apparatus (forexample, a stepper) of another type or a lithography apparatus ofanother type such as an imprint apparatus. In this case, the lithographyapparatus is an apparatus for forming a pattern on a substrate, and theconcept includes an exposure apparatus, an imprint apparatus, and anelectron beam drawing apparatus.

An article manufacturing method of manufacturing an article (forexample, a semiconductor IC element, a liquid crystal display element,or a MEMS) using the above-described lithography apparatus will bedescribed below. The article manufacturing method can be a method thatincludes a transfer step of transferring a pattern of an original to asubstrate using the lithography apparatus, and a processing step ofprocessing the substrate having undergone the transfer step, therebyobtaining an article from the substrate having undergone the processingstep.

When the lithography apparatus is an exposure apparatus, the articlemanufacturing method can include a step of exposing a substrate (asubstrate, a glass substrate, or the like) coated with a photosensitiveagent, a step of developing the substrate (photosensitive agent), and astep of processing the developed substrate in other known steps. Theother known steps include etching, resist removal, dicing, bonding, andpackaging. According to this article manufacturing method, ahigher-quality article than a conventional one can be manufactured. Whenthe lithography apparatus is an imprint apparatus, the articlemanufacturing method can include a step of forming a pattern made of acured product of an imprint material by molding the imprint material ona substrate using a mold, and a step of processing the substrate usingthe pattern.

While the present invention has been described with reference toexemplary embodiments, it is to be understood that the invention is notlimited to the disclosed exemplary embodiments. The scope of thefollowing claims is to be accorded the broadest interpretation so as toencompass all such modifications and equivalent structures andfunctions.

This application claims the benefit of Japanese Patent Application No.2021-113751, filed Jul. 8, 2021, which is hereby incorporated byreference herein in its entirety.

What is claimed is:
 1. A processing apparatus comprising: a driverconfigured to drive a controlled object; and a controller configured tocontrol the driver by generating a command value to the driver based ona control error, wherein the controller includes: a first compensatorconfigured to generate a first command value based on the control error,a second compensator configured to generate a second command value basedon the control error, and an adder configured to obtain the commandvalue by adding the first command value and the second command value,the second compensator includes a neural network for which a parametervalue is decided by learning, and input parameters input to the neuralnetwork include a driving condition of the driver and an environmentcondition in a periphery of the controlled object in addition to thecontrol error.
 2. The apparatus according to claim 1, wherein thedriving condition and the environment condition as the input parametersare pieces of information that are not input to the first compensator.3. The apparatus according to claim 1, wherein the driving conditionincludes at least one of a position, a driving direction, a drivingstroke, a speed, an acceleration, a jerk, and a snap of the controlledobject.
 4. The apparatus according to claim 1, wherein the environmentcondition includes at least one of a pressure, a temperature, and avibration in the periphery of the controlled object.
 5. A managementapparatus for managing a processing apparatus defined in claim 1, themanagement apparatus comprising: a learning device configured toredecide the parameter value by learning based on a control result ofthe controlled object by the controller, wherein the learning devicerepeats the learning while changing at least one of the drivingcondition and the environment condition.
 6. The apparatus according toclaim 5, wherein in a case that a reward required from the controlresult of the controlled object by the controller does not satisfy acriterion, the learning device repeats the learning while changing atleast one of the driving condition and the environment condition.
 7. Theapparatus according to claim 6, wherein in a case that the rewardrequired from the control result of the controlled object by thecontroller does not satisfy the criterion, the learning device repeatsthe learning while changing a combination pattern among a first numberof first combination patterns of the driving condition and theenvironment condition, and in a case that the reward exceeds apredetermined value while the learning is repeated, the learning devicerepeats the learning while changing a combination pattern among a secondnumber of second combination patterns, which is larger than the firstnumber.
 8. A lithography apparatus for performing processing oftransferring a pattern of an original to a substrate, comprising: adriver configured to drive a controlled object for the processing; and acontroller configured to control the driver by generating a commandvalue to the driver based on a control error, wherein the controllerincludes: a first compensator configured to generate a first commandvalue based on the control error, a second compensator configured togenerate a second command value based on the control error, and an adderconfigured to obtain the command value by adding the first command valueand the second command value, the second compensator includes a neuralnetwork for which a parameter value is decided by learning, and isconfigured to control the driver using the neural network, and inputparameters input to the neural network include a driving condition ofthe driver and an environment condition in a periphery of the controlledobject in addition to the control error.
 9. An article manufacturingmethod comprising: transferring a pattern of an original to a substrateusing a lithography apparatus defined in claim 8; and processing thesubstrate having undergone the transferring, wherein an article isobtained from the substrate having undergone the processing.